Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Decision Making01:20

Decision Making

1.0K
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
1.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.0K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
7.0K
Drug Distribution: Volume of Distribution01:25

Drug Distribution: Volume of Distribution

7.6K
The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.
7.6K
F Distribution01:19

F Distribution

10.7K
The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
10.7K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.5K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.5K
Distributed Loads01:19

Distributed Loads

981
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
981

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Stochastic choice drives variability in patch foraging decisions in humans and rats.

Communications psychology·2026
Same author

The Computational Bottleneck of Basal Ganglia Output (and What to Do About it).

eNeuro·2025
Same author

Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2024
Same author

Tracking subjects' strategies in behavioural choice experiments at trial resolution.

eLife·2024
Same author

Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.

bioRxiv : the preprint server for biology·2023
Same author

Differential Dopamine Receptor-Dependent Sensitivity Improves the Switch Between Hard and Soft Selection in a Model of the Basal Ganglia.

Neural computation·2022

Related Experiment Video

Updated: Feb 12, 2026

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K

A probabilistic, distributed, recursive mechanism for decision-making in the brain.

Javier A Caballero1,2, Mark D Humphries1, Kevin N Gurney2

  • 1Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

Plos Computational Biology
|April 4, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel Bayesian algorithm to understand brain decision-making. This recursive model accurately simulates monkey behavior on a sensory task and maps to brain circuitry, offering insights into neural computations.

More Related Videos

Quantifying the Heterogeneous Distribution of a Synaptic Protein in the Mouse Brain Using Immunofluorescence
09:18

Quantifying the Heterogeneous Distribution of a Synaptic Protein in the Mouse Brain Using Immunofluorescence

Published on: January 29, 2019

8.6K
Combined Mechanical and Enzymatic Dissociation of Mouse Brain Hippocampal Tissue
07:14

Combined Mechanical and Enzymatic Dissociation of Mouse Brain Hippocampal Tissue

Published on: October 21, 2021

4.7K

Related Experiment Videos

Last Updated: Feb 12, 2026

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
11:51

Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

Published on: March 2, 2011

15.7K
Quantifying the Heterogeneous Distribution of a Synaptic Protein in the Mouse Brain Using Immunofluorescence
09:18

Quantifying the Heterogeneous Distribution of a Synaptic Protein in the Mouse Brain Using Immunofluorescence

Published on: January 29, 2019

8.6K
Combined Mechanical and Enzymatic Dissociation of Mouse Brain Hippocampal Tissue
07:14

Combined Mechanical and Enzymatic Dissociation of Mouse Brain Hippocampal Tissue

Published on: October 21, 2021

4.7K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Decision formation involves multiple brain regions, but the precise computational mechanism remains unclear.
  • Understanding how sensory information is integrated into a decision is a key challenge in neuroscience.

Purpose of the Study:

  • To elucidate the computational principles underlying decision formation in the brain.
  • To propose and validate a novel algorithm that explains neural and behavioral data during decision-making tasks.

Main Methods:

  • Developed a recursive Bayesian algorithm processing spike trains with sensory cortex statistics.
  • Simulated the random-dot-motion task to compare algorithm predictions with monkey choice behavior.
  • Mapped the algorithm's architecture to known cortico-basal-ganglia-thalamo-cortical loops.
  • Analyzed neural activity dynamics in sensorimotor cortex and striatum.

Main Results:

  • The algorithm quantitatively replicated monkey choice behavior in the simulated task.
  • Predicted information loss from sensory cortex (MT) during decision formation.
  • Algorithm architecture mapped to recurrent brain loops (cortico-basal-ganglia-thalamo-cortical).
  • Simulated computational dynamics matched neural activity in sensorimotor cortex and striatum, and predicted basal ganglia and thalamus activity.

Conclusions:

  • The proposed recursive Bayesian algorithm provides a unified framework for understanding decision formation.
  • The brain likely implements a probabilistic, distributed, recursive, and parallel mechanism for decision-making.
  • The algorithm's success suggests its core principles reflect the brain's actual decision-making machinery.